The Material Intensity of Growth: Implications from the Human Scale of Production

Contemporary empirical studies on the resource intensity of the economic process provide evidence of a gradual de-linking between natural resources use and economic growth. Resource intensity is evaluated through the Domestic Material Consumption/Gross Domestic Product (DMC/GDP) ratio, defined as the material intensity index. Trajectories of this ratio support the optimistic view that economic output is becoming progressively less dependent on resource flows, hence GDP is gradually dematerialized. The present study asserts that the DMC/GDP indicator fails to take into account the biophysical properties of the production process which define the resource requirements of the economy. The present study proposes the “resources required for producing one unit of GDP per Capita (Income)”, as an alternative indicator for evaluating the resource requirements of the economy. The resource requirement, evaluated at the level of income, approximates the human scale of production; goods should embody certain biophysical properties in order to satisfy human needs. The trajectories of DMC/Income index for global growth rejects the vision of a dematerialized growth and the de-linkage of the economy from natural resources.     

Customer‐Base Concentration and Inventory Efficiencies: Evidence from the Manufacturing Sector

What is the link between customer‐base concentration and inventory efficiencies in the manufacturing sector? Using hand‐collected data from 10‐K Filings, we find that manufacturers with more concentrated customer bases hold fewer inventories for less time and are less likely to end up with excess inventories, as indicated by the lower likelihood and magnitude of inventory write‐downs and reversals. Using disaggregated inventory disclosures, we find that inventory efficiencies primarily flow through the finished goods inventory account, while raw material efficiencies are offset by higher work‐in‐process holdings and longer work‐in‐process cycles. In additional analysis, we document a valuation premium for more concentrated manufacturers after controlling for other firm characteristics, including default risk and cost of capital estimates. We conclude that investors trade off the costs and benefits of relationships with a limited number of major customers and, on balance, consider customer‐base concentration as a net positive for firm valuation. Overall, our study adds to interdisciplinary research in accounting and operations management by shedding new light on the relevance of major customer disclosures for fundamental analysis and valuation in the manufacturing sector.     

Total energy optimal multicasting in wireless ad hoc networks

In this paper, we present our novel algorithm, SOR (Shrinking Overlapped Range), for the minimum energy multicasting in wireless ad hoc networks. The heuristics in the literature have not considered changing the intermediate tree structure and this may result in worse performance even after local improvements at the end. In SOR, we extensively change the intermediate tree structure to maintain tighter structure in terms of energy consumption. We do so by shrinking the overlapped transmission range following the idea of WMA (wireless multicast advantage) property and by allowing the selection of internal transmissions which further changes the tree structure. Both theoretical analysis and experimental results show SOR outperforms other heuristics in the literature. Research is partially supported by NSF and Air Force grants.     

Multiple phase neighborhood Search—GRASP based on Lagrangean relaxation, random backtracking Lin–Kernighan and path relinking for the TSP

In this paper, a new modified version of Greedy Randomized Adaptive Search Procedure (GRASP), called Multiple Phase Neighborhood Search—GRASP (MPNS-GRASP), is proposed for the solution of the Traveling Salesman Problem. In this method, some procedures have been included to the classical GRASP algorithm in order to improve its performance and to cope with the major disadvantage of GRASP which is that it does not have a stopping criterion that will prevent the algorithm from spending time in iterations that give minor, if any, improvement in the solution. Thus, in MPNS-GRASP a stopping criterion based on Lagrangean Relaxation and Subgradient Optimization is proposed. Also, a different way for expanding the neighborhood search is used based on a new strategy, the Circle Restricted Local Search Moves strategy. A new variant of the Lin-Kernighan algorithm, called Random Backtracking Lin-Kernighan that helps the algorithm to diversify the search in non-promising regions of the search space is used in the Expanding Neighborhood Search phase of the algorithm. Finally, a Path Relinking Strategy is used in order to explore trajectories between elite solutions. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results.     

Improvements to MCS algorithm for the maximum clique problem

In this paper we present improvements to one of the most recent and fastest branch-and-bound algorithm for the maximum clique problem—MCS algorithm by Tomita et al. (Proceedings of the 4th international conference on Algorithms and Computation, WALCOM’10, pp. 191–203, 2010). The suggested improvements include: incorporating of an efficient heuristic returning a high-quality initial solution, fast detection of clique vertices in a set of candidates, better initial colouring, and avoiding dynamic memory allocation. Our computational study shows some impressive results, mainly we have solved p_hat1000-3 benchmark instance which is intractable for MCS algorithm and got speedups of 7, 3000, and 13000 times for gen400_p0.9_55, gen400_p0.9_65, and gen400_p0.9_75 instances correspondingly.     

A Branch and Cut solver for the maximum stable set problem

This paper deals with the cutting-plane approach to the maximum stable set problem. We provide theoretical results regarding the facet-defining property of inequalities obtained by a known project-and-lift-style separation method called edge-projection, and its variants. An implementation of a Branch and Cut algorithm is described, which uses edge-projection and two other separation tools which have been discussed for other problems: local cuts (pioneered by Applegate, Bixby, Chvátal and Cook) and mod-k cuts. We compare the performance of this approach to another one by Rossi and Smiriglio (Oper. Res. Lett. 28:63–74, 2001) and discuss the value of the tools we have tested.     

A rearrangement of adjacency matrix based approach for solving the crossing minimization problem

In this paper, we first present a binary linear programming formulation for the crossing minimization problem (CMP) in bipartite graphs. Then we use the models of a modified minimum cost flow problem (MMCF) and a travelling salesman problem (TSP) to approximatively solve the CMP by rearranging the adjacency matrix of the bipartite graph. Our approaches are useful for problems defined on dense bipartite graphs. In addition, we compute the exact crossing numbers for some general dense graphs.     

Offshore Outsourcing,Yield Uncertainty,and Contingency Responses

In this article, we study an offshore outsourcing arrangement for a buyer of a produced good in the presence of supply yield uncertainty. We analyze the performance of contingency responses to the realized yield information at the end of production and prior to its delivery to the destination market. The contingency responses considered are: (i) Emergency Production via which an emergency order is placed with another fast and perfectly reliable offshore supplier; (ii) Emergency Production and Delivery via which an expedited shipping of (partial or total) good units is used on top of Emergency Production. Within a periodic review inventory system with uncertain demand setting, we theoretically characterize the optimal decisions on the cycle order size, the emergency order size, and the way to split the available good units between the fast and slow shipping modes. We provide comparative statics on how the choices of these quantities are affected by each other and by the demand and yield uncertainties. We use numerical examples to illustrate the values of such contingency responses and the impact of other factors on the cost of meeting demand.     

Flexible Capacity Investments and Product Mix: Optimal Decisions and Value of Postponement Options

In this article, we study a firm's interdependent decisions in investing in flexible capacity, capacity allocation to individual products, and eventual production quantities and pricing in meeting uncertain demand. We propose a three‐stage sequential decision model to analyze the firm's decisions, with the firm being a value maximizer owned by risk‐averse investors. At the beginning of the time horizon, the firm sets the flexible capacity level using an aggregate demand forecast on the envelope of products its flexible resources can accommodate. The aggregate demand forecast evolves as a Geometric Brownian Motion process. The potential market share of each product is determined by the Multinomial Logit model. At a later time and before the end of the time horizon, the firm makes a capacity commitment decision on the allocation of the flexible capacity to each product. Finally, at the end of the time horizon, the firm observes the demand and makes the production quantity and pricing decisions for end products. We obtain the optimal solutions at each decision stage and investigate their optimal properties. Our numerical study investigates the value of the postponed capacity commitment option in supplying uncertain operation environments.     

Minimum ε-equivalent Circuit Size Problem

For a Boolean function f given by its truth table (of length ) and a parameter s the problem considered is whether there is a Boolean function g -equivalent to f, i.e., , and computed by a circuit of size at most s. In this paper we investigate the complexity of this problem and show that for specific values of it is unlikely to be in P/poly. Under the same assumptions we also consider the optimization variant of the problem and prove its inapproximability.